基于规则和统计的中文自动文摘系统

自动文摘是自然语言处理领域里一个重要课题,本文在传统方法基础上提出了一种中文自动文摘的方法。在篇章结构分析里,我们提出了基于连续段落相似度的主题划分算法,使生成的文摘更具内容全面性与结构平衡性。同时结合了若干规则对生成的文摘初稿进行可读性加工处理,使最终生成的文摘更具可读性。最后提出了一种新的文摘评价方法(F-new-measure)对系统进行测试。系统测试表明该方法在不同文摘压缩率时,评价值均较为稳定。

Research on Automatic Summarization Based on Rules and Statistics for Chinese Texts

As automatic summarization is an important research topic in the natural language processing,the paper presents an approach for Chinese text summarization on the basis of traditional methods.For text structure analysis,an algorithm is proposed for multi-topic text partitioning based on sequential paragraphic similarity,which can makes the abstract of the multi-topic article have more general content and more balanced structure.Futhermore,a series of rules are combined to enhance the readability of the output abstract.Finally,a new evaluation method is put forward.The primary test shows that its value is stable.